Subsampling Method for Robust Estimation of Regression Models

Min Tsao, Xiao Ling

Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada V8W 3P4.
Department of Mathematics and Statistics, University of Victoria, Victoria, Canada.

DOI: 10.4236/ojs.2012.23034                

Abstract

We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation; it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.

Keywords

Subsampling Algorithm; Robust Regression; Outliers; Bootstrap; Goodness-of-Fit

Conflicts of Interest

The authors declare no conflicts of interest.

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